Ståhl, Niclas

University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.

Falkman, Göran

University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.

Mathiason, Gunnar

University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.

Karlsson, Alexander

University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.

2018 (English)Conference paper, Published paper (Refereed)

Abstract [en]

We present a new self-organizing algorithm for classification of a data that combines and extends the strengths of several common machine learning algorithms, such as algorithms in self-organizing neural networks, ensemble methods and deep neural networks. The increased expression power is combined with the explanation power of self-organizing networks. Our algorithm outperforms both deep neural networks and ensembles of deep neural networks. For our evaluation case, we use production monitoring data from a complex steel manufacturing process, where data is both high-dimensional and has many nonlinear interdependencies. In addition to the improved prediction score, the algorithm offers a new deep-learning based approach for how computational resources can be focused in data exploration, since the algorithm points out areas of the input space that are more challenging to learn.

Abstract [en]

Smart Cities have emerged as a global concept that argues for the effective exploitation of digital technologies to drive sustainable innovation and well-being for citizens. Despite the large investments being placed on Smart City infrastructure, however, there is still very scarce attention on the new learning approaches that will be needed for cultivating Digital Smart Citizenship competences, namely the competences which will be needed by the citizens and workforce of such cities for exploiting the digital technologies in creative and innovative ways for driving financial and societal sustainability. In this context, this paper introduces cyberphysical learning as an overarching model of cultivating Digital Smart Citizenship competences by exploiting the potential of Internet of Things technologies and social media, in order to create authentic blended and augmented learning experiences.

Abstract [en]

With the evolution of modern Critical Infrastructures (CI), more Cyber-Physical systems are integrated into the traditional CIs. This makes the CIs a multidimensional complex system, which is characterized by integrating cyber-physical systems into CI sectors (e.g., transportation, energy or food & agriculture). This integration creates complex interdependencies and dynamics among the system and its components. We suggest using a model with a multi-dimensional operational specification to allow detection of operational threats. Embedded (and distributed) information systems are critical parts of the CI where disruption can lead to serious consequences. Embedded information system protection is therefore crucial. As there are many different stakeholders of a CI, comprehensive protection must be viewed as a cross-sector activity to identify and monitor the critical elements, evaluate and determine the threat, and eliminate potential vulnerabilities in the CI. A systematic approach to threat modeling is necessary to support the CI threat and vulnerability assessment. We suggest a Threat Graph Model (TGM) to systematically model the complex CIs. Such modeling is expected to help the understanding of the nature of a threat and its impact on throughout the system. In order to handle threat cascading, the model must capture local vulnerabilities as well as how a threat might propagate to other components. The model can be used for improving the resilience of the CI by encouraging a design that enhances the system's ability to predict threats and mitigate their damages. This paper surveys and investigates the various threats and current approaches to threat modeling of CI. We suggest integrating both a vulnerability model and an attack model, and we incorporate the interdependencies within CI cross CI sectors. Finally, we present a multi-dimensional threat modeling approach for critical infrastructure protection.

Mathiason, Gunnar

University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.

2009 (English)Doctoral thesis, monograph (Other academic)

Abstract [en]

A fully replicated distributed real-time database provides high availability and predictable access times, independent of user location, since all the data is available at each node. However, full replication requires that all updates are replicated to every node, resulting in exponential growth of bandwidth and processing demands with the number of nodes and objects added. To eliminate this scalability problem, while retaining the advantages of full replication, this thesis explores Virtual Full Replication (ViFuR); a technique that gives database users a perception of using a fully replicated database while only replicating a subset of the data.

We use ViFuR in a distributed main memory real-time database where timely transaction execution is required. ViFuR enables scalability by replicating only data used at the local nodes.Also, ViFuR enables flexibility by adaptively replicating the currently used data, effectively providing logical availability of all data objects. Hence, ViFuR substantially reduces the problem of non-scalable resource usage of full replication, while allowing timely execution and access to arbitrary data objects.

In the thesis we pursue ViFuR by exploring the use of database segmentation. We give a scheme (ViFuR-S) for static segmentation of the database prior to execution, where access patterns are known a priori. We also give an adaptive scheme (ViFuR-A) that changes segmentation during execution to meet the evolving needs of database users. Further, we apply an extended approach of adaptive segmentation (ViFuR-ASN) in a wireless sensor network - a typical dynamic large-scale and resource-constrained environment. We use up to several hundreds of nodes and thousands of objects per node, and apply a typical periodic transaction workload with operation modes where the used data set changes dynamically. We show that when replacing full replication with ViFuR, resource usage scales linearly with the required number of concurrent replicas, rather than exponentially with the system size.

Abstract [en]

It is important for the life time of a wireless sensor network (WSN) to reduce the amount of data transferred through the network. As a typical approach, sensor data is filtered before propagating updates, to a node at the edge of a network, where it can be fused. Information Fusion inside the network can reduce the amount of data propagated, by fusing data before and in propagation, without losing the information value in it. We explore infrastructures for distributed fusion, with fusion nodes located at strategic nodes inside the network, as an approach of structured distributed fusion for WSNs. We propose an infrastructure for a white-board approach that uses a distributed real-time database with virtual full replication. With such an approach, both raw and fused data are logically available at all nodes and physically available where used, such that only used data will be propagated and use resources. The actual resource usage will be relative to the actual demand for data, rather than to the amount of data published at the white-board. We present an exploration of such an infrastructure, and points out future key research questions for such a white-board approach.

Abstract [en]

Sensor networks have limited resources and often support large-scale applications that need scalable propagation of sensor data to users. We propose a white-board style of communication in sensor networks using a distributed real-time database supporting Virtual Full Replication with Adaptive Segmentation. This allows mobile client nodes to access, transparently and efficiently, any sensor data at any node in the network. We present a two-tiered wireless architecture, and an adaptation protocol, for scalable and adaptive white-board communication in large-scale sensor networks. Sensor value readings at nodes of the sensor tier are published at nodes of the database tier as database updates to objects in a distributed real-time database. The search space of client nodes for sensor data is thus limited to the number of database nodes. With this scheme, we can show scalable resource usage and short adaptation times for several hundreds of database nodes and up to 50 moving clients.